Prof. Yan Zhang

University of Oslo

Title: Machine Learning in Digital Twin Edge Networks


Yan Zhang is a Full Professor at Department of Informatics, University of Oslo, Norway. He received a Ph.D. degree in School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore. He received M.S. and B.S from Beihang University and Nanjing University of Post and Telecommunications, respectively. His current research interests include: 6G and Internet of Things (e.g., transport, smart grid). He received the prestigious award Clarivate Analytics (previously Thomson Reuters) “Highly Cited Researcher” since 2018.

He is IEEE Fellow and IET Fellow. He is an elected Fellow of Academia Europaea (MAE), an elected Fellow of Royal Norwegian Society of Sciences and Letters Academy (DKNVS) and an elected Fellow of Norwegian Academy of Technological Sciences (NTVA). He serves as the Chair of IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) during 2019-2021. He is IEEE Communication Society Distinguished Lecturer and IEEE Vehicular Technology Society (VTS) Distinguished Speaker. He was IEEE VTS Distinguished Lecturer for two terms (2016-2018 and 2018-2020). He is a member of IEEE VTS Fellowship and Scholarship Committee. He is currently serving as an Area Editor/Senior Editor/Editor of 11 IEEE Transactions/Magazines, including IEEE Communications Magazine; IEEE Network Magazine; IEEE Communications Surveys & Tutorials; IEEE Transactions on Network Science and Engineering; IEEE Transactions on Industrial Informatics; IEEE Transactions on Vehicular Technology; IEEE Transactions on Green Communications and Networking; IEEE Internet of Things Journal; IEEE Systems Journal; IEEE Vehicular Technology Magazine and IEEE Blockchain Technical Briefs.


In this talk, we mainly introduce our proposed new research direction: Digital Twin Edge Networks (DITEN). We first present the concept and model related to Digital Twin (DT) and DITEN. Then, we focus on new research challenges and results when machine learning is exploited in DITEN, including federated learning, deep reinforcement learning and transfer learning. Edge association and DT mobility, as unique research questions, will be defined and analyzed. We are also expecting that the talk will help the audience understand the future development of edge computing, e.g., digital twin edge networks in the context of Metaverse.

Prof. Shiwen Mao

Auburn University

Title: Technology-agnostic RF sensing for human activity recognition


SHIWEN MAO is a professor and Earle C. Williams Eminent Scholar Chair, and Director of the Wireless Engineering Research and Education Center (WEREC) at Auburn University. His research interest includes wireless networks, multimedia communications, and smart grid. He is a Distinguished Lecturer of IEEE Communications Society and the IEEE Council of RFID, and is on the Editorial Board of IEEE TWC, IEEE TNSE, IEEE TMC, IEEE IoT, IEEE TCCN, IEEE OJ-ComSoc, IEEE/CIC China Communications, IEEE Multimedia, IEEE Network, IEEE Networking Letters, and ACM GetMobile. He received the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019 and NSF CAREER Award in 2010. He is a co-recipient of the 2021 Best Paper Award of Elsevier/KeAi Digital Communications and Networks Journal, the 2021 IEEE Communications Society Outstanding Paper Award, the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award, the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems, and several conference best paper/demo awards. He is a Fellow of the IEEE.


In recent years, 3D human activity recognition (HAR) has become an important topic in human-computer interaction (HCI). To improve the privacy of users, there is considerable interest in techniques without using a video camera. Various radio-frequency (RF) sensing technologies, such as WiFi, Radio-Frequency Identification (RFID), and Frequency-Modulated Continuous Wave (FMCW) radar, have been utilized for non-invasive human activity recognition (HAR). It will be highly desirable to develop a HAR solution that can work with different types of RF technologies, such that the cost and the barrier of wide deployment can both be greatly reduced, and more robust performance can be achieved by utilizing the complementary RF sensory data. In this talk, we present a technology-agnostic approach for RF-based HAR, termed TARF, which works with several different RF sensing technologies. A novel data generalization technique is proposed to mitigate the disparity in measured data from different RF devices. A domain adversarial neural network is proposed to combat the interference from various RF sensing technologies. The performance of the proposed system is evaluated with experiments using four different RF sensing technologies. TARF is shown to outperform the state-of-the-art Convolutional Neural Network (CNN)-based solution with considerable gains.

Prof. Jingyu Yang

Tianjin University

Title: Intelligent Image Restoration: From random noise to structural degradation


Jingyu Yang received the B.E. degree from the Beijing University of Posts and Telecommunications, Beijing, China, in 2003, and the Ph.D. degree (Hons.) from Tsinghua University, Beijing, in 2009. He has been a Faculty Member with Tianjin University, Tianjin, China, since 2009, where he is currently a Professor with the School of Electrical and Information Engineering. He was with Microsoft Research Asia (MSRA), Beijing, in 2011, within the MSRAs Young Scholar Supporting Program, and with the Signal Processing Laboratory, ´ Ecole Polytechnique F´ed´eralee Lausanne, Lausanne, Switzerland, in 2012 and from 2014 to 2015. His research interests include image video processing, 3-D imaging, and computer vision. Dr. Yang served as the Special Session Chair in the International Conference on Visual Communications and Image Processing 2016 and the Area Chair in the International Conference on Image Processing 2017. He was selected in the program for New Century Excellent Talents in University (NCET) from the Ministry of Education, China, in 2011, the Reserved Peiyang Scholar Program of Tianjin University in 2014, and the Tianjin Municipal Innovation. Talent Promotion Program in 2015.


The contamination of images and videos are ubiquitous in storage, transmission, or manipulation of image data. One of central tasks in the field of image restoration is to recover clean ones from noisy data. Most existing image restoration methods assume observation models with random noise. However, structural degradation, such as bursty missing in transmission, frame loss in video capture, and the lack of high resolution information in undersampling, can also occur frequently in various vison applications. The simplified noise models would introduce mode mismatch and affect the overall restoration performance. In this talk, I am going to present our recent work in the field of image restoration, particularly some interesting investigation on restoration from moiré artifacts, which are much more difficult to handle than random noise.

Prof. Zhangjie Fu

Nanjing University of Information Science & Technology

Title: Digital forensics technology


Zhangjie Fu, Ph.D, Professor, Doctoral supervisor. He is currently filling the post of A.P. of School of Computer, Software and Cyberspace security, Nanjing University of Information Science and Technology, and Executive Director of Engineer Research Center of Digital Forensic Ministry of Education. His main research interests are blockchain security, digital forensics and more. In recent years, he has presided 5 National Programs including National Key R&D Program of China, and has published more than 50 papers in top periodicals and conferences as first author or corresponding author and been invited to give speeches in multiple conferences at home and abroad. He is also the president and program committee president of multiple conferences at home or abroad including 2020 China Cryptologic Application Summit Forum, the subeditor and editorial committee member of multiple periodicals at home or abroad.


Digital forensics technology is the crossover research and application of computer subjects and law. As a necessary technology support, it plays a very important role in cyberspace security governance. Now, one problem that urgently needed to be solved has appeared in this field: How to realize the combination of advanced computer and digital forensics technology under the multimedia environment with data explosion and rapidly changed technology, and use it to collect digital criminal evidences efficiently? In this report, we will first introduce the development situation of digital content forensics technology at home and abroad, then we will analysis the challenges and difficulties existed in current digital forensics field. At last, we will show the research result in multimedia content forensics that we have achieved.